GenAI (Comprehensive)

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Course Overview

  1. This course is meant for Developers and will use lot of coding in Python. This will explain what is Gen AI, and how to use Gen AI.
  2. This will excplain how to use Gen AI via UI, code, and also how to pass additional context to avoid hallucination and certainty of results.
  3. This will aslo explain how to build a LLM application in many ways
  4. This course was conducted in many product companies and was well received

At the end of the training, participants will be able to:

None

Pre-requisite

Must know basic of ML, what ML], DL and RNN deos, No need to know in depth and certaininly not Maths. I will briefly review this. But must be handson in Python

Duration

None

Course Outline

  1. What is ML, SML, USML, RL
  2. What is DL
  3. What is NLP
  1. What is Generative AI – Text, Image, Audio, Video
  2. Gen AI Tools: Chat GPT, Chat GPTPlus, Bard, Gemini, Dall E
  3. Gen AI Tool Demonstrations
  1. What is Transformer and why it became popular
    Limiatations of RNN based architecture and how transformer helped them
  2. Trasnformer Architecture
  3. Models derived from Trasnformer
  1. What is LLM
  2. How can we use LLM?
  3. Common Gen AI LLMs – GPT3.5, GPT 4, LLaMa, Gemma
  1. Gen AI Tools and applications,
  2. Common use cases of NLP that can be solve dusing Gen AI
  3. How to use ChatGpt
  4. Chat GPT Architectures and engines used.
  5. Prompt Engineering
  6. Zero Shot, One Shot, Few Shot In context Learning
  7. Solve some case studies using ChatGPT
  1. Via Prompt Engineering UI
  2. Via Prompt Engineering and code
  3. Solve some Prompt Engineering via API
  4. Create Embeddings using LLM and consume embeddings
  5. Fine Tune LLM – Model Adapting, RLHF,
  6. Create LLM
  1. Key Problems – Hallucination, Data Privacy
  2. Embeddigns and how/where to use them
  3. HuggingFace frameworks and use that to solve some problems
  4. Using RAG
  5. Using Vector DB, Intro to FAISS, Weaviate
  6. Query a Vecotr DB and pass context to LLM to address Hallucination
  7. Solve some unsupervised clustering problems using these embeddings and HuggingFace
  8. What else can we do with LLM – LLM + DT/CNN/RNN*

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